I wonder if there is any statistical program that could help me to calculate power and/or sample size for (generalized) linear mixed models without requiring programming knowledge.
G*Power is also useful:
https://www.psychologie.hhu.de/arbeitsgruppen/allgemeine-psychologie-und-arbeitspsychologie/gpower
I recommended
https://www.danielsoper.com/statcalc/default.aspx
http://sample-size.net/means-effect-size/
Hi,
G Power is a free source sample size calculator software that has a lot of options.
Richard E Gilder and Bhogaraju Anand , you both have recommended G*Power. Please say how it is used for linear mixed models.
The questionnaire was asking for a menu driven software and I find G power a reasonably good program for this purpose. Also, refer to the following :
https://stats.stackexchange.com/questions/423499/power-analysis-in-g-power-mixed-model-anova.
Yes, R programs have more options and flexibility only when people get familiar with this program.
I am open to getting educated on this subject from our esteemed statisticians.
Bhogaraju Anand , I was asking you how to do power for a mixed (multilevel) model with G*Power, not for you to say what G*Power or R are. Given you responded to this question asking for doing a mixed linear model power analysis, you should be willing to tell the questioner how, and I'd like to be educated too!
Daniel Wright, as far as I know, G*Power doesn't have that functionality, and I guess you are aware of that. I also suspect that Gilder and Anand just mentioned their best-known sample size tool without bothering whether it really does the specific job asked for... Apologies if i'm wrong!
Thomas Grischott , I fear you may be correct. I would hope if somebody is answering someone's question that they would not be trying to lead them astray, but I don't know. But, maybe they know something that I don't about G*Power, and I am happy to learn. If they are putting wrong answer out there, it would be interesting to know why. This often happens with researchgate and this means that people should really be skeptical about answers.
Can I have references from literature for the statement made by Grisschott "G*Power doesn't have that functionality, and I guess you are aware of that. I also suspect that Gilder and Anand just mentioned their best-known sample size tool without bothering whether it really does the specific job asked for..."
UCLA stat consulting makes a statement in their write up as "Among the programs specifically designed for power analysis, we use SPSS Sample Power, PASS and GPower. These programs have a friendly point-and-click interface and will do power analyses for things like correlations, OLS regression and logistic regression." "For more advanced/complicated analyses, Mplus is a good choice."
Ref:https://stats.idre.ucla.edu/other/mult-pkg/seminars/intro-power/
G*Power has been suggested on this very Research gate by some statisticians previously as a useful tool.
If you can show deficiencies in G*Power and also suggest alternatives it would help us to become knowledgeable. After all, this is a platform for the exchange of ideas and learning.
Here is a paper on utility of G*Power:
https://link.springer.com/content/pdf/10.3758%2FBF03203630.pdf
Bhogaraju Anand: The question was not whether G*Power is a useful tool or not, but whether it can be used for mixed models. The UCLA statement does not mention anything of that kind. The paper that you cited mentions "approximate F tests for fixed factors in mixed models" but does not elaborate further. If you have aditional information, please share. Otherwise, Daniel Wright's answer pointed to an alternative.
You can do a power analysis in SAS, R, Python, Stata, etc. for Mixed models. Depending on nthe complexity of your model (factors, restriction on randomization) it might get involved. What you need is an estimate of the difference you would like to detect and scale-appropriate error variances mean that for an RCB with a split plot restriction on randomization you would need estiamtes for both mainplot and subplot error. If you had a RCB with a split block restriction on randomization you would need three error terms. In the process you will need to calculate the non-centraility parameter for F. Warning: The overall power of the test is rather meaningless. What you want is the power to detect certain differnces; the latter will always be smaller than the former.
Hi, Daniel Wright . As I have seen, MLPowSim comes with no warranty. Does it mean that results might be less accurate? Please, let me know about your experience with that program.
I read about the online calculator GLIMMPSE. It seems to include general (not generalized) linear mixed models, but it may be useful. Does anybody know about it? It seems easy to use, but I don't know if it is reliable.
Hi, Richard E Gilder and Bhogaraju Anand . Thank you both for your answers, but, as far as I know, G*Power doesn't accomplish linear mixed models analyses. And in the user's guide they doesn't appear either. Thank you for your advice too, Thomas Grischott and Daniel Wright
I did read about PASS too, but it isn't free. I'm trying the 30-day trial version, but it seems difficult to me since I have to pick the specific analysis model. Does anybody know about some useful guide?
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